CN114571285B - Method for intelligently identifying micro-tipping of extrusion tap - Google Patents
Method for intelligently identifying micro-tipping of extrusion tap Download PDFInfo
- Publication number
- CN114571285B CN114571285B CN202210216847.8A CN202210216847A CN114571285B CN 114571285 B CN114571285 B CN 114571285B CN 202210216847 A CN202210216847 A CN 202210216847A CN 114571285 B CN114571285 B CN 114571285B
- Authority
- CN
- China
- Prior art keywords
- current load
- extrusion tap
- tap
- tipping
- extrusion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001125 extrusion Methods 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000012545 processing Methods 0.000 claims abstract description 14
- 238000003754 machining Methods 0.000 claims description 13
- 238000005259 measurement Methods 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 description 12
- 238000005299 abrasion Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 238000010079 rubber tapping Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
- B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
- B23Q17/0957—Detection of tool breakage
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23G—THREAD CUTTING; WORKING OF SCREWS, BOLT HEADS, OR NUTS, IN CONJUNCTION THEREWITH
- B23G1/00—Thread cutting; Automatic machines specially designed therefor
- B23G1/16—Thread cutting; Automatic machines specially designed therefor in holes of workpieces by taps
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Machine Tool Sensing Apparatuses (AREA)
- Extrusion Moulding Of Plastics Or The Like (AREA)
Abstract
The invention discloses a method for intelligently identifying micro-tipping of an extrusion tap, which comprises the following steps: step S1, establishing an extrusion tap state judgment model; step S2, collecting a current load value of a main shaft of a servo motor during the extrusion tap processing; s3, comparing and judging the current load value with a extrusion tap state judgment model; and S4, outputting preset information according to the judging result.
Description
Technical Field
The invention relates to the field of machining, in particular to a method for intelligently identifying micro-tipping of an extrusion tap.
Background
In the core part of the steering system of the automobile part and the tapping process of the double-end internal thread on the rack, an extruding tap is needed to process the internal thread of the bottom hole which is pre-drilled, in the process, the extrusion of the processed material is carried out under the working condition of the rotary feeding of the specific tap to form an internal thread hole, the whole process is only carried out for 2 seconds, and the state of the tap in the whole life cycle is good and bad, so that the quality of the processed product is directly influenced.
At present, the prior art aims at monitoring the tap processing process, only can identify the breakage of a cutter, and cannot monitor the abnormal wear, tiny tipping and other slight changes of the cutter, and the changes can possibly cause the abnormal occurrence of an internal threaded hole of a processed part and the rejection of batch parts. If the rated life of the tool is reduced, effective control of the quality of the part being machined is possible, but the cost for machining is increased by 30% or more. The technical problems to be solved are as follows: how to identify the abnormal abrasion and tiny tipping of the tap during the processing process, thereby avoiding the scrapping of the processed parts.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for intelligently identifying micro-tipping of an extrusion tap, which comprises the following steps:
step S1, establishing an extrusion tap state judgment model;
step S2, collecting a current load value of a main shaft of a servo motor during the extrusion tap processing;
s3, comparing and judging the current load value with a extrusion tap state judgment model;
and S4, outputting preset information according to the judging result.
Preferably, the method for establishing the state judgment model of the extrusion tap in the step S1 is as follows: collecting current load values of a main shaft of a servo motor in the life cycle machining process of a single extrusion tap, and integrating the current load values by using a data fitting method to obtain a current load set value; collecting measurement data of the extrusion tap machined part; correlating the current load set value with measurement data of the part to form a current load set value curve; and collecting N sets of current load set values and measurement data of the parts, and establishing a extrusion tap state judgment model.
Preferably, the measured data of the part is an effective thread depth dimension of the threaded bore in the part.
Preferably, in said step S1, each set of said current load set values and measurement data of said part is also marked for correlating a machining process and a machining count.
Preferably, the N is greater than 100.
Preferably, the current load set value volatility curve of the current load value during a single tap life cycle is calculated from the current load set value.
Preferably, in the step S3, when a value in the current load set value fluctuation curve exceeds a first threshold of the judgment model, the step S4 outputs early warning information.
Preferably, in the step S3, when the value in the current load set value fluctuation curve exceeds the second threshold value of the judgment model, the step S4 outputs alarm information to prompt that the extruding tap needs to be replaced.
Compared with the prior art, the invention automatically collects the current load value of the main shaft of the servo motor and judges the health state of the extrusion tap by establishing the judging model, thereby being capable of timely identifying the abnormal abrasion and the tiny tipping of the extrusion tap in the processing process and avoiding the scrapping of the processed parts.
Drawings
FIG. 1 is a schematic diagram of method steps for intelligently identifying micro-tipping of an extrusion tap;
FIG. 2 is a graph of the current load on the servo motor spindle during single part machining of the extrusion tap;
FIG. 3 is a graph of current load set values during a single full life cycle of a tap;
FIG. 4 is a graph of current load set value fluctuation rate during a single full life cycle of a tap.
Detailed Description
As shown in fig. 1, the present embodiment provides a method for intelligently identifying micro-tipping of a tap, which includes:
step S1, establishing an extrusion tap state judgment model;
step S2, collecting a current load value of a main shaft of a servo motor during the extrusion tap processing;
s3, comparing and judging the current load value with a extrusion tap state judgment model;
and S4, outputting preset information according to the judging result.
How to build the extrusion tap state judgment model is as follows:
it was found by study that the micro-chipping state of the tap was related to the current load of the servo spindle rotation motor of the driving part of the tap. When the tap is in a state of no abnormal abrasion or no micro-tipping, each machined part of the current load of the servo spindle is not changed greatly, and the overlapping ratio is very high.
Fig. 2 is a graph of current load on a servo motor spindle during single part machining of an extrusion tap, wherein the abscissa is a machining time axis and the ordinate is a current load value. The whole processing process of the tap is divided into a feeding stage, a reversing stage and a retracting stage according to the processing time
The curves of the three states of the healthy tap (without abnormal abrasion), the micro-tipping tap and the severe tipping tap are listed in fig. 2, and the obvious difference between the taps in different states in the feeding stage can be obviously seen.
According to the tiny difference, the possibility of micro-tipping of the screw tap in different processing life stages is matched, and according to the measured effective thread depth dimension of the internal threaded hole of the part, whether the real-time state of the screw tap is healthy or micro-tipping or severe tipping can be effectively identified in the actual processing environment.
Collecting current load values of a main shaft of a servo motor in the life cycle machining process of a single extrusion tap, and integrating the current load values by using a data fitting method to obtain a current load set value; collecting measurement data of the extrusion tap machined part; correlating the current load set value with the measured data of the part to form a life fluctuation curve of the current load set value, wherein the abscissa is an accumulated life value and the ordinate is the current load set value as shown in fig. 3; and collecting more than 100 sets of current load set values and measurement data of the parts, and establishing a extrusion tap state judgment model. Each set of current load set values and measured data of the part is also marked for correlating process and process counts. The state of the cutter in the normal state and the state of the abnormal cutter are included.
Preferably, the current load set value is also calculated from the current load set value as shown in fig. 4 for the rate of fluctuation of the current load set value during the life cycle of the single tap.
FIG. 4 is a graph of current load set rate of fluctuation during the full life cycle of a single extrusion tap, with the abscissa indicating the processing life of each tap and the ordinate indicating the current load set rate of fluctuation. And outputting early warning information when the value (namely the value of the ordinate) in the current load set value fluctuation curve exceeds the first threshold value of the judgment model. And when the value (namely the value of the ordinate) in the current load set value fluctuation curve exceeds the second threshold value of the judgment model, outputting alarm information to prompt that the extruding tap needs to be replaced. In this embodiment, the first threshold is 60 and the second threshold is 80.
The normal tap current load characteristic and the curve model of variance and service life thereof are calculated by an intelligent algorithm or a traditional regression analysis method. On the basis, the characteristic value of the current load curve in real-time processing is extracted by the same method, the distance between the characteristic value and the reference curve is calculated, the result is divided by N times of mean square difference value, and the judgment threshold value is obtained after the result is converted into percentage. Here, N is 3.3, and according to the noise analysis theory, the range can cover 96% points of white noise; the value should be reasonably taken according to the actual situation. In case the training sample is large enough, the value can also be determined by intelligent algorithms.
By the establishment of the model, the abnormal wear state of the tap can be effectively identified, the original current load value data required by the establishment of the model can be collected through an establishment program, or the current load value data can be collected through a special current load acquisition card and transmitted to an edge computer through a PLC. Measurement data of the part after the tap is machined is collected by synchronizing through a factory data center. The summarized data is converted into an effective and simplified data stream through data cleaning.
The data stream is imported into the model in real time, real-time cutter states are displayed through model judgment, when the model judges abnormal values of the cutters, reminding information is sent to a PLC alarm module at the equipment end, different status lamps are displayed according to different output conclusions, and an operator timely checks or replaces the tap according to the prompts of the status lamps.
According to the method, current load value data of a rotating spindle servo motor in the tap machining process are collected and utilized, meanwhile, actual service life of the tap is counted, thread measurement depth data of an internal threaded hole of a machined part are attached to conduct compound analysis, each tap is labeled in the early stage, so that an effective mathematical model is built, an effective recognition mode can be formed by utilizing the basic model, the iteration model and the multi-source data, the mode can be carried on an edge computer, judgment is conducted on each tap machining process, whether abnormal changes such as abnormal abrasion and micro-tipping exist in the tap at present can be effectively recognized, and the abnormal changes are fed back to a PLC (programmable logic controller) through the computer to conduct lamp-on early warning.
Claims (7)
1. The method for intelligently identifying the micro-tipping of the extrusion tap is characterized by comprising the following steps of:
step S1, establishing an extrusion tap state judgment model;
step S2, collecting a current load value of a main shaft of a servo motor during the extrusion tap processing;
s3, comparing and judging the current load value with a extrusion tap state judgment model;
s4, outputting preset information according to a judging result;
the method for establishing the extrusion tap state judgment model in the step S1 is as follows:
collecting current load values of a main shaft of a servo motor in the life cycle processing process of a single extrusion tap, integrating the current load values by using a data fitting method to obtain a current load set value,
collecting measurement data of the extrusion tap machined part;
correlating the current load set value with measurement data of the part to form a current load set value curve;
and collecting N sets of current load set values and measurement data of the parts, and establishing a extrusion tap state judgment model.
2. The method of intelligently identifying micro-tipping of an extrusion tap of claim 1, wherein:
the measured data of the part is the effective thread depth dimension of the threaded hole in the part.
3. The method of intelligently identifying micro-tipping of an extrusion tap of claim 1, wherein:
in said step S1, each set of said current load set values and measurement data of said part is also marked for correlating a machining process and a machining count.
4. The method of intelligently identifying micro-tipping of an extrusion tap of claim 1, wherein:
the N is greater than 100.
5. The method of intelligently identifying micro-tipping of an extrusion tap of claim 1, wherein:
and calculating according to the current load set value to obtain a current load set value fluctuation rate curve of the current load value in the life cycle process of the single extrusion tap.
6. The method of intelligently identifying micro-tipping of an extrusion tap of claim 5, wherein:
in the step S3, when the value in the current load set value fluctuation rate curve exceeds the first threshold of the judgment model, the step S4 outputs early warning information.
7. The method of intelligently identifying micro-tipping of an extrusion tap of claim 5, wherein:
in the step S3, when the value in the current load set value fluctuation rate curve exceeds the second threshold value of the judgment model, the step S4 outputs alarm information to prompt that the extrusion tap needs to be replaced.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210216847.8A CN114571285B (en) | 2022-03-07 | 2022-03-07 | Method for intelligently identifying micro-tipping of extrusion tap |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210216847.8A CN114571285B (en) | 2022-03-07 | 2022-03-07 | Method for intelligently identifying micro-tipping of extrusion tap |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114571285A CN114571285A (en) | 2022-06-03 |
| CN114571285B true CN114571285B (en) | 2024-01-19 |
Family
ID=81772828
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210216847.8A Active CN114571285B (en) | 2022-03-07 | 2022-03-07 | Method for intelligently identifying micro-tipping of extrusion tap |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114571285B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116415434B (en) * | 2023-04-07 | 2024-05-24 | 平湖市山特螺纹工具有限公司 | Screw tap processing technique and system for high-strength steel |
| CN116908677A (en) * | 2023-06-06 | 2023-10-20 | 华能上海燃机发电有限责任公司 | Circulating water filter screen on-line monitoring system and method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002239838A (en) * | 2001-02-15 | 2002-08-28 | Tokyo Taping Machine Seisakusho:Kk | Abnormality monitoring method in female threading process |
| CN202726639U (en) * | 2012-08-14 | 2013-02-13 | 沈阳机床（集团）设计研究院有限公司 | Cutting condition monitoring system based on computerized numerical control (CNC) milling machine spindle servo motor current signal |
| CN107414600A (en) * | 2017-05-04 | 2017-12-01 | 南京航空航天大学 | The process monitoring method of internal thread low frequency exciting cold extrusion lathe based on multiple sensor signals |
| CN107584334A (en) * | 2017-08-25 | 2018-01-16 | 南京航空航天大学 | A kind of complex structural member numerical control machining cutter status real time monitor method based on deep learning |
| CN109262368A (en) * | 2018-09-13 | 2019-01-25 | 成都数之联科技有限公司 | A kind of tool failure determination method |
| CN109262369A (en) * | 2018-09-13 | 2019-01-25 | 成都数之联科技有限公司 | A kind of cutting tool state detection system and method |
| CN109277882A (en) * | 2018-09-25 | 2019-01-29 | 江苏西格数据科技有限公司 | A kind of machine tool monitoring system |
| CN109909804A (en) * | 2018-12-21 | 2019-06-21 | 北京工业大学 | Tool breakage and abrasion on-line monitoring method based on main shaft drives electric current and work step |
| CN110421408A (en) * | 2019-07-02 | 2019-11-08 | 绍兴安迪自动化设备有限公司 | A kind of monitoring method of cutting-tool wear state |
-
2022
- 2022-03-07 CN CN202210216847.8A patent/CN114571285B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002239838A (en) * | 2001-02-15 | 2002-08-28 | Tokyo Taping Machine Seisakusho:Kk | Abnormality monitoring method in female threading process |
| CN202726639U (en) * | 2012-08-14 | 2013-02-13 | 沈阳机床（集团）设计研究院有限公司 | Cutting condition monitoring system based on computerized numerical control (CNC) milling machine spindle servo motor current signal |
| CN107414600A (en) * | 2017-05-04 | 2017-12-01 | 南京航空航天大学 | The process monitoring method of internal thread low frequency exciting cold extrusion lathe based on multiple sensor signals |
| CN107584334A (en) * | 2017-08-25 | 2018-01-16 | 南京航空航天大学 | A kind of complex structural member numerical control machining cutter status real time monitor method based on deep learning |
| CN109262368A (en) * | 2018-09-13 | 2019-01-25 | 成都数之联科技有限公司 | A kind of tool failure determination method |
| CN109262369A (en) * | 2018-09-13 | 2019-01-25 | 成都数之联科技有限公司 | A kind of cutting tool state detection system and method |
| CN109277882A (en) * | 2018-09-25 | 2019-01-29 | 江苏西格数据科技有限公司 | A kind of machine tool monitoring system |
| CN109909804A (en) * | 2018-12-21 | 2019-06-21 | 北京工业大学 | Tool breakage and abrasion on-line monitoring method based on main shaft drives electric current and work step |
| CN110421408A (en) * | 2019-07-02 | 2019-11-08 | 绍兴安迪自动化设备有限公司 | A kind of monitoring method of cutting-tool wear state |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114571285A (en) | 2022-06-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114571285B (en) | Method for intelligently identifying micro-tipping of extrusion tap | |
| CN109909804B (en) | Tool wear damage online monitoring method based on spindle driving current and process steps | |
| DE102017011290B4 (en) | Machine learning apparatus, CNC apparatus and machine learning method for detecting an indication of chatter occurrence in a machine tool tool | |
| Stavropoulos et al. | Monitoring and control of manufacturing processes: A review | |
| CA1215158A (en) | Tool wear and tool failure monitor system | |
| US20030163286A1 (en) | Tool abnormality detecting device | |
| US7571022B2 (en) | System and method for monitoring machine health | |
| CN107186547A (en) | Numerical control turning batch machining tool wear on-line monitoring method based on cutting power | |
| CN110421408B (en) | Method for monitoring wear state of cutter | |
| EP1650622A1 (en) | Data management and networking system and method | |
| CN111761411A (en) | Cutter health prediction device and method based on vibration signals | |
| CN114800040B (en) | Cutter wear monitoring method and system related to process-state data | |
| CN113305644A (en) | Cutter state monitoring and early warning method and system based on part measurement data | |
| CN115169050A (en) | Dynamic prediction method for abrasion loss of cutting tool with multi-model fusion | |
| CN113627304A (en) | Machine tool spindle health monitoring method and system based on artificial intelligence | |
| CN114326593B (en) | Cutter life prediction system and method | |
| CN111308960A (en) | Load monitoring method and system | |
| CN106371407A (en) | Statistic method for machining working state and machining robot | |
| Watanabe et al. | Anomaly detection methods in turning based on motor data analysis | |
| CN113478496A (en) | Lathe and robot integrated application system | |
| CN112859741A (en) | Method and system for evaluating operation reliability of sequential action units of machine tool | |
| CN117313009A (en) | Fault prevention method based on machining center equipment data | |
| Noh et al. | Implementation of remote monitoring system for prediction of tool wear and failure using ART2 | |
| JP2575323B2 (en) | Cutting load monitoring method by data extraction averaging method | |
| CN114547900A (en) | Method for evaluating running state of motor of automatic production line |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |